Imitating object movement skills with robots &amp;#x2014; A task-level approach exploiting generalization and invariance

Gienger, Michael; Mühlig, Manuel; Steil, Jochen J.

doi:10.1109/iros.2010.5649990

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…In this work, DMPs are constructed from single segments that came from the task configuration most similar to the current one that the robot faces. However, there exist more sophisticated methods involving dynamic time warping [20] and Bayesian techniques [26] to perform LfD with many demonstration segments. Using such techniques, it may be possible to create more robust skill models that can be used in an ever-increasing number of complex situations, allowing end-users to program robots with ease.…”

Section: Discussionmentioning

confidence: 99%

“…This algorithm also uses heuristic segmentation and cannot recognize repeated instances of skills. Gienger et al [20] segment skills based on comovement between the demonstrator's hand and objects in the world and automatically find appropriate task-space abstractions for each skill. Their method can generalize skills by identifying task frames of reference, but cannot describe skills like gestures or actions in which the relevant object does not move with the hand.…”

Section: Related Workmentioning

confidence: 99%

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Learning and generalization of complex tasks from unstructured demonstrations

Niekum

Osentoski

Konidaris

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

143

103

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We present a novel method for segmenting demonstrations, recognizing repeated skills, and generalizing complex tasks from unstructured demonstrations. This method combines many of the advantages of recent automatic segmentation methods for learning from demonstration into a single principled, integrated framework. Specifically, we use the Beta Process Autoregressive Hidden Markov Model and Dynamic Movement Primitives to learn and generalize a multi-step task on the PR2 mobile manipulator and to demonstrate the potential of our framework to learn a large library of skills over time.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Learning and generalization of complex tasks from unstructured demonstrations

Niekum

Osentoski

Konidaris

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

143

103

View full text Add to dashboard Cite

show abstract

“…For each recorded demonstration with time stamps t t t ∈ R N , we assume that its corresponding reproduction have the same duration with N number of timesteps. This assumption can be straightforwardly fulfilled by applying data processing methods such as Dynamic Time Warping [16].…”

Section: A Gps As Wrench Modelsmentioning

confidence: 99%

Learning Movement Assessment Primitives for Force Interaction Skills

Zhang,

Polydoros,

Piater

2018

Preprint

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We present a novel, reusable and task-agnostic primitive for assessing the outcome of a force-interaction robotic skill, useful e.g. for applications such as quality control in industrial manufacturing. The proposed method is easily programmed by kinesthetic teaching, and the desired adaptability and reusability are achieved by machine learning models. The primitive records sensory data during both demonstrations and reproductions of a movement. Recordings include the endeffector's Cartesian pose and exerted wrench at each time step. The collected data are then used to train Gaussian Processes which create models of the wrench as a function of the robot's pose. The similarity between the wrench models of the demonstration and the movement's reproduction is derived by measuring their Hellinger distance. This comparison creates features that are fed as inputs to a Naive Bayes classifier which estimates the movement's probability of success. The evaluation is performed on two diverse robotic assembly tasks -snap-fitting and screwing -with a total of 5 use cases, 11 demonstrations, and more than 200 movement executions. The performance metrics prove the proposed method's capability of generalization to different demonstrations and movements.

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“…This algorithm also uses heuristic segmentation and cannot recognize repeated instances of skills. Gienger et al [2010] segment skills based on co-movement between the demonstrator's hand and objects in the world and automatically find appropriate task-space abstractions for each skill. Their method can generalize skills by identifying task frames of reference, but cannot describe skills like gestures or actions in which the relevant object does not move with the hand.…”

Section: Skill Learning From Demonstrationmentioning

confidence: 99%

Learning grounded finite-state representations from unstructured demonstrations

Niekum

Osentoski

Konidaris³

et al. 2014

The International Journal of Robotics Research

169

160

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Robots exhibit flexible behavior largely in proportion to their degree of knowledge about the world. Such knowledge is often meticulously hand-coded for a narrow class of tasks, limiting the scope of possible robot competencies. Thus, the primary limiting factor of robot capabilities is often not the physical attributes of the robot, but the limited time and skill of expert programmers. One way to deal with the vast number of situations and environments that robots face outside the laboratory is to provide users with simple methods for programming robots that do not require the skill of an expert.For this reason, learning from demonstration (LfD) has become a popular alternative to traditional robot programming methods, aiming to provide a natural mechanism for quickly teaching robots. By simply showing a robot how to perform a task, users can easily demonstrate new tasks as needed, without any special knowledge about the robot. Unfortunately, LfD often yields little knowledge about the world, and thus lacks robust generalization capabilities, especially for complex, multi-step tasks.We present a series of algorithms that draw from recent advances in Bayesian nonparametric statistics and control theory to automatically detect and leverage repeated structure at multiple levels of abstraction in demonstration data. The discovery of repeated structure provides critical insights into task invariants, features of importance, high-level task structure, and appropriate skills for the task. This culminates in the discovery of a finite-state representation of the task, comprised of grounded skills that are flexible and reusable, providing robust generalization and transfer in complex, multistep robotic tasks. These algorithms are tested and evaluated using a PR2 mobile manipulator, showing success on several complex real-world tasks, such as furniture assembly.

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Imitating object movement skills with robots — A task-level approach exploiting generalization and invariance

Cited by 10 publications

References 20 publications

Learning and generalization of complex tasks from unstructured demonstrations

Learning and generalization of complex tasks from unstructured demonstrations

Learning Movement Assessment Primitives for Force Interaction Skills

Learning grounded finite-state representations from unstructured demonstrations

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